TX IQ mismatch pre-compensation

ABSTRACT

A direct conversion wireless transmitter includes IQ mismatch pre-compensation using direct learning adaptation to adjust IQ pre-compensation filtering. Widely-linear IQ_mismatch pre-compensation filtering compensates for IQ mismatch in the TX analog chain, filtering of input data x(n) to provide pre-compensated data y(n) with a compensation image designed to interfere destructively with the IQ_mismatch image. A feedback receiver FBRX captures feedback data z(n) used for direct learning adaptation. DL adaptation adjusts IQ_mismatch filters, modeled as an x(n)_direct and complex conjugate x(n)_image transfer functions w1 and w2, including generating an adaptation error signal based on a difference between TX/FBRX-path delayed versions of x(n) and z(n), and can include estimation and compensation for TX/FBRX phase errors. DL adaptation adjusts the IQ pre-comp filters w1/w2 to minimize the adaptation error signal. Similar modeling can be used for IQ mismatch. The IQ_mismatch pre-compensator can be implemented as a combination of digital signal processing and hardware acceleration.

CROSS-REFERENCE TO RELATED APPLICATIONS

Priority is claimed under 37 CFR 1.78 and 35 USC 119(e) to U.S.Provisional Application 62/040,775, filed 22 Aug. 2014, which isincorporated by reference.

BACKGROUND

Technical Field

This Patent Document relates generally to direct-conversion wirelesstransmitter design, including IQ mismatch compensation.

Related Art

In wireless transceivers, direct conversion can be used for thetransmitter (TX) and/or receiver (RX). Direct conversion (zero/low IF)wireless architectures use IQ modulation/demodulation and directupconversion/downconversion to/from RF, eliminating conversion at anintermediate frequency (IF).

Direct conversion architectures commonly use quadrature (IQ) signalconversion and digital filtering. To meet requirements on out-of-bandemissions, direct conversion transmitter designs commonly use digitalcompensation for TX non-linearities and IQ mismatch (mismatch/imbalancebetween I and Q signal paths).

TX non-linearities can be compensated by digital pre-distortion (DPD).IQ mismatch is compensated by digital filtering (IQ mismatchcompensation or QMC). A feedback receiver (FBRX) is used to capture datarequired for such compensation.

IQ mismatch generates an image at frequencies reflected about the LO(local oscillator) frequency, which can appear in frequency bandsoutside the channel reserved for the TX (direct) signal. QMC is used tomeet spectral emissions mask requirements for out-of-band interference,such as ACLR (adjacent channel leakage ratio) and ACPR (adjacent channelpower ratio).

Various approaches to adapting TX QMC filter coefficients either makeassumptions about the IQ mismatch, or restrict the form of thetransmitted signal band. For example, TX QMC filter coefficients can beadapted assuming the TX IQ mismatch is frequency-independent, or that itdoes not drift with temperature. The frequency-independent assumption isnot satisfied for transmitters that need to handle broadband signals,such as multi-carrier LTE deployments. Solutions that assume themismatch does not drift (for example, due to ambient temperaturecontrol) can use a one-time calibration with a broadband training signalduring system power up.

Other approaches are able to adapt the TX IQ mismatch over a widebandwidth, and track temperature variations as long as the signalspectrum is restricted to a symmetric band. These approaches require asingle carrier or regularly spaced channels.

BRIEF SUMMARY

This Brief Summary is provided as a general introduction to theDisclosure provided by the Detailed Description and Drawings,summarizing aspects and features of the Disclosure. It is not a completeoverview of the Disclosure, and should not be interpreted as identifyingkey elements or features of, or otherwise characterizing or delimitingthe scope of, the disclosed invention.

The Disclosure describes apparatus and methods for TX IQ mismatchpre-compensation using direct learning adaptation, suitable for use in adirect conversion wireless transmitter architecture.

According to aspects of the Disclosure, a direct conversion wirelesstransmitter can include IQ mismatch pre-compensation using directlearning adaptation to adjust IQ pre-compensation filtering.Widely-linear IQ_mismatch pre-compensation filtering compensates for IQmismatch in the TX analog chain, filtering input data x(n) to providepre-compensated data y(n) with a compensation image designed tointerfere destructively with the IQ_mismatch image. A feedback receiverFBRX captures feedback data z(n) used for direct learning adaptation. DLadaptation adjusts IQ_mismatch filters, modeled as an x(n)_direct andcomplex conjugate x(n)_image transfer functions w1 and w2, includinggenerating an adaptation error signal based on a difference betweenTX/FBRX-path delayed versions of x(n) and z(n), and can includeestimation and compensation for TX/FBRX phase errors. DL adaptationadjusts the IQ pre-comp filters w1/w2 to minimize the adaptation errorsignal. Similar modeling can be used for IQ mismatch. The IQ_mismatchpre-compensator can be implemented as a combination of digital signalprocessing and hardware acceleration.

Other aspects and features of the invention claimed in this PatentDocument will be apparent to those skilled in the art from the followingDisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example wireless transceiver (100) including adirect conversion wireless transmitter (TX 110) with a TX IQ_MismatchPre-Compensator (120) and a Feedback Receiver (FBRX 140).

FIG. 2 illustrates an example TX IQ_Mismatch Pre-Compensator (220),including IQ pre-comp filters (221) (direct and complex conjugate imagetransfer functions w1 and w2), with direct learning adaptation (222) ofthe IQ pre-comp filters (w1/w2), including transmit/feedback signal pathdelay compensation (223/224).

FIG. 3 illustrates an example TX IQ_Mismatch Pre-Compensator (220),including IQ pre-comp filters (w1/w2 (direct and complex conjugatetransfer functions w1 and w2), with direct learning adaptation (222),including transmit/feedback signal path delay compensation (223/224),and including phase error estimation/compensation (225).

DETAILED DESCRIPTION

This Description and the Drawings constitute a Disclosure for TX IQmismatch pre-compensation using direct learning adaptation for use in adirect conversion wireless transmitter architecture, including designexamples that illustrate various technical features and advantages.

In brief overview, IQ mismatch pre-compensation using direct learningadaptation can be used in an direct conversion wireless transmitter toadjust IQ pre-compensation filtering. Widely-linear IQ_mismatchpre-compensation filtering compensates for IQ mismatch in the TX analogchain, filtering of input data x(n) to provide pre-compensated data y(n)with a compensation image designed to interfere destructively with theIQ_mismatch image. A feedback receiver FBRX captures feedback data z(n)used for direct learning adaptation. DL adaptation adjusts IQ_mismatchfilters, modeled as an x(n)_direct and complex conjugate x(n)_imagetransfer functions w1 and w2, including generating an adaptation errorsignal based on a difference between TX/FBRX-path delayed versions ofx(n) and z(n), and can include estimation and compensation for TX/FBRXphase errors. DL adaptation adjusts the IQ pre-comp filters w1/w2 tominimize the adaptation error signal. Similar modeling can be used forIQ mismatch. The IQ_mismatch pre-compensator can be implemented as acombination of digital signal processing and hardware acceleration.

FIG. 1 illustrates an example wireless transceiver 100 that includes atransmit (TX) signal chain/path 102, a feedback receive (FBRX) signalchain/path 103, and a receive (RX) signal chain/path 106.

TX signal chain 102 is based on a direct conversion (zero/low-IF)architecture. It includes a direct conversion transmitter TX 110providing IQ modulation and upconversion of a TX baseband analog signalto TX RF. The TX analog chain is characterized by an IQmismatch/imbalance associated with IQ modulation and upconversion. IQmismatch is manifested as an IQ_mismatch image that, without QMC, willappear in TX RF.

An IQ_Mismatch Pre-Compensator 120 in the TX signal chain 102pre-filters input digital TX baseband data x(n), producingpre-compensated TX baseband data y(n). IQ_Mismatch Pre-Compensator 120adaptively filters the input TX baseband data x(n) such that theresulting pre-compensated TX baseband data y(n) provided to the TXanalog chain manifests a compensation image designed to interferedestructively with the IQ_mismatch image associated with IQ mismatch inthe TX analog chain, suppressing the IQ_mismatch image from the TX RF(below adjacent channel signal interference requirements). A feedbackreceiver FBRX 130 in the FBRX signal path 103 captures data used byIQ_Mismatch Pre-compensator 120 for IQ_mismatch pre-compensation,converting the TX RF back to digital FBRX baseband data z(n).

The example transmitter TX 110 includes an IQ filter front end 112, anda TX upconverter 114 with IQ mixers 116I/116Q. IQ filter front endreceives the pre-compensated TX baseband data y(n), providing IQfiltering for the real I and imaginary Q portions of the TX basebanddata y(n), with DAC conversion to corresponding analog TX IQ basebandsignals. TX upconverter 114 low-pass filters the TX IQ baseband signals,followed by upconversion to RF in IQ mixers 116I/116Q and summing.

RF circuitry 150 provides RF transmit and receive. TX RF is transmitted,and fed back to the FBRX signal path 103 (FBRX 130). Received RX RF isrouted to the RX signal path 106, including a receiver RX 160.

As described in detail in connection with FIGS. 2 and 3, TX IQ_MismatchPre-Compensator 120 implements widely linear pre-compensation filteringwith direct learning adaptation according to this Disclosure.

Pre-compensation filtering is widely linear in that the input TXbaseband data x(n) and its complex conjugate are filtered.Pre-compensation filtering is modeled as an x(n)_direct transferfunction w1 (receiving x(n) as input), and a complex conjugatex(n)_image transfer function w2 (receiving a complex conjugate of x(n)as input). Transfer functions w1 and w2 are designated IQ pre-compfilters.

IQ pre-comp filters w1 and w2 are adapted based on direct learning inthat the IQ_Mismatch Pre-Compensator directly adjusts the IQ pre-compfilters (filter coefficients) in the signal path.

The example transceiver 100 in FIG. 1 is functionally illustrated with adirect conversion TX signal chain 102, including transmitter 110 andIQ_Mismatch Pre-Compensator 120, and associated FBRX signal chain 103including feedback receiver FBRX 130. A separate RX signal path 106includes receiver RX 160.

FBRX 130 and RX 160 can be based on direct conversion or heterodynearchitectures. If FBRX 130 is a direct conversion architecture, itshould be designed to provide the FBRX baseband data z(n), includingdownconversion mixing and IQ demodulation, without exhibitingsignificant IQ mismatch, such as by implementing IQ mismatchpre-compensation. FBRX 130 and RX 160 can be implemented as a sharedreceiver architecture.

FIG. 2 illustrates an example TX IQ_Mismatch Pre-Compensator 220,configured for IQ pre-compensation filtering with direct learningadaptation. IQ_Mismatch Pre-Compensator 220 includes IQ pre-compensationfiltering 221, modeled as an x(n)_direct transfer function w1 (receivingx(n) as input), and a complex conjugate x(n)_image transfer function w2(receiving a complex conjugate of x(n) as input), designated as IQpre-comp filters w1 and w2.

The example IQ_Mismatch Pre-Compensator 220 is implemented by digitalsignal processing, such as in a digital signal processor (DSP) ormicroprocessor unit (MPU), in combination with hardware acceleration forcertain functions that are computationally expensive. For example,hardware acceleration can be used for auto-correlation of x(n) and z(n),and cross-correlation between x(n) and z(n).

IQ_Mismatch Pre-Compensator 220 receives input TX baseband data x(n),and feedback FBRX baseband data z(n), and produces pre-compensated TXbaseband data y(n). IQ_Mismatch Pre-Compensator 220 implements IQpre-compensation filtering 221, with direct learning filter adaptation222. The pre-compensated TX baseband data y(n) is input to TX 110 for IQmodulation and upconversion to RF, providing IQ_mismatch imagesuppression through destructive interference with the y(n) compensationimage (below any adjacent channel signal interference requirements).

The example IQ_Mismatch Pre-Compensator 220 adjusts the IQ pre-compfilters w1 and w2 based on a linear combination of direct learningadaptations for successive data blocks. IQ pre-comp filters w1 and w2are fixed during data block capture. For each data block, IQ_MismatchPre-compensator 220 performs direct learning adaptation, whichadaptations are stored for linear combination before the IQ pre-compfilters are adjusted.

Direct learning adaptation 222 adapts the IQ pre-comp filters w1/w2 as adirect learning adaptation based on the input TX baseband data x(n) andfeedback FBRX baseband data z(n), including estimating delay through therespective TX and FBRX signal chains (FIGS. 1, 102 and 103).

Functionally, IQ_Mismatch Pre-Compensator 220 includes a TX delayestimator 223 and a FBRX delay estimator 224. TX delay estimator 223modifies the input TX baseband data x(n) corresponding to a delaythrough the TX signal path, producing a delayed version of x(n). FBRXdelay estimator 224 modifies feedback FBRX baseband data z(n)corresponding to a delay through the FBRX signal path, producing adelayed version of z(n).

A TX/FBRX error signal generator 226, such as a signal subtractor,generates an adaptation error signal 227 corresponding to a differencebetween respective delayed versions of x(n) and z(n).

A DL adapter 229 implements a direct learning adaption of theIQ_mismatch pre-compensation filters w1 and w2, based on the adaptationerror signal 227. DL adapter 229 performs direct learning adaptation toadjust IQ pre-comp filters w1 and w2 (filter coefficients) to minimizethe adaptation error signal.

As noted above, the example IQ_Mismatch Pre-Compensator 220 isimplemented by digital signal processing in combination with hardwareacceleration for certain functions that are computationally expensive.For example, hardware acceleration can be used for auto-correlation ofx(n) and z(n), and cross-correlation between x(n) and z(n). Further,hardware acceleration can be used for some combination of TX delayestimation, FBRX delay estimation, TX/FBRX error signal generation, andDL adaptation.

DL adapter 226 can be configured to minimize the adaptation error (andachieve convergence of the IQ pre-comp filters w1/w2 that converges inthe direction of an estimated steepest descent, according to the vectorexpression w[n]=w[n−1]−μΔw[n], where w[n] is the filter state vector forthe filter update, and Δw[n] is a steepest descent vector for theestimated direction of the steepest descent. The steepest descent vectorΔw[n] is related to an error vector (e[n]) for the adaptation errorsignal by a Jacobian matrix, denoted e=J(Δw).

The update to the filter state vector can be based on a least-squaressolution, expressed as w[n+1]=w[n]−μ(J^(H)J)⁻¹(J^(H)e[n]), where[Δw[n]=(J^(H)J)⁻¹(J^(H)e[n])] is a least-squares estimation for thedirection of steepest descent. Alternative approaches to determining thefilter state vector include using a conjugate gradient algorithm, andusing a Kalman filter.

Convergence of the direct learning adaptation of the IQ pre-comp filtersw1/w2 depends in part on the design of the TX analog chain. IQ_mismatchassociated with TX 110 can be modeled by a y(n)_direct transfer functiong₁(f) ((receiving y(n) as input), and a complex conjugate y(n)_imagetransfer function g₂(f) (receiving a complex conjugate of y(n) asinput). TX 110 (FIG. 1, analog chain 114) can be designed such thatdirect learning adaption converges based on: (a) ∥g₁(f)∥≈1, (b) ∥g₂(f)∥is in the range of 30 dB less than ∥g₁(f)∥, and (c) ∥1−g₁(f)∥<1.

Referring to FIG. 1, implementations of the TX and FBRX signal chainscan cause phase variations that require compensation. For example, TX110 can be designed for multiple transmit channels (multiple carrierfrequencies), and for multiple calibration functions, with FBRX 130shared between channels and calibration functions. In addition, FBRX canbe used to capture data for both IQ mismatch compensation in the TXsignal chain, and DPD (digital pre-distortion) for the power amplifier.When the FBRX carrier frequency alternates, the phase difference betweenTX and FBRX mixers changes.

FIG. 3 illustrates an example TX IQ_Mismatch Pre-Compensator (220) inwhich phase error can be estimated and corrected in the correlationcomputation for direct learning adaptation.

TX IQ_Mismatch Pre-Compensator 220, direct learning adaptation 222, caninclude phase error estimation 225. Specifically, direct learningadaptation 222 can be configured to estimate a phase error between theTX upconversion and FBRX downconversion, represented as phase errorestimator 225. DL adapter 229 can implement direct learning adaptationincluding phase error compensation according to the phase of a complexnumber generated in DL adapter 229 by a cross-correlation between the TXbaseband data x(n) and the FBRX baseband data z(n).

TX IQ mismatch compensation with direct learning adaptation of IQpre-compensation filtering according to this Disclosure has a number ofadvantages. Direct learning adaptation can compensate and track fullyfrequency-dependent TX IQ mismatch, independent of the frequency band ofthe transmitted signal. A widely-linear digital filter design is used tomitigate interference by producing an image in the baseband (digital)domain that cancels the IQ_mismatch image resulting from IQ mismatch inthe TX analog chain, so that any resulting image is below adjacentchannel signal interference requirements. IQ pre-compensation filtercoefficients are adapted to handle arbitrary broadband TX signalspectra, and to track mismatch variations due to factors such astemperature drift.

The Disclosure provided by this Description and the Figures sets forthdesign examples and applications illustrating aspects and features ofthe invention, and does not limit the scope of the invention, which isdefined by the claims. Known circuits, functions and operations are notdescribed in detail to avoid obscuring the principles and features ofthe invention. These design examples and applications can be used byordinarily skilled artisans as a basis for modifications, substitutionsand alternatives to construct other designs, including adaptations forother applications.

The invention claimed is:
 1. A wireless transmitter (TX) circuit basedon a direct conversion architecture for use in a system including afeedback receiver circuit (FBRX), the TX circuit comprising a TX(transmit) signal chain coupled to receive digital TX baseband datax(n), and including a digital TX IQ_mismatch pre-compensator tocompensate for in-phase (I) and quadrature (Q) mismatch, the TXIQ_mismatch pre-compensator coupled to receive the digital TX basebanddata x(n) and provide digital pre-compensated TX baseband data y(n) byperforming IQ pre-compensation filtering based on IQ pre-comp filters w1and w2; an analog TX RF (radio frequency) unit to generate anupconverted TX RF signal based on the pre-compensated TX baseband datay(n); and an adaptation module coupled to the TX IQ_mismatchpre-compensator to perform direct learning adaptation of the IQ pre-compfilters w1 and w2, modeled respectively as an x(n)_direct transferfunction w1 receiving x(n) as input, and a complex conjugate x(n)_imagetransfer function w2 receiving a complex conjugate of x(n) as input, theadaptation module coupled to receive the TX baseband data x(n), andcorresponding FBRX baseband data z(n) from the FBRX based ondownconverting the TX RF signal; the adaptation module including: a TXdelay estimator configured to modify x(n) corresponding to a delaythrough the TX RF unit, producing a delay x(n) signal; a FBRX delayestimator configured to modify z(n) corresponding to a delay through theFBRX unit, producing a delay z(n) signal; a TX/FBRX error signalgenerator configured to generate an adaptation error signalcorresponding to a difference between the delay x(n) and delay z(n)signals; and a DL (direct learning) adapter to perform direct learningadaptation to adjust the IQ pre-comp filters w1 and w2 to minimize theadaptation error signal.
 2. The circuit of claim 1, wherein the DLadapter is configured perform a direct learning adaptation thatconverges in the direction of an estimated steepest descent, accordingto, where w[n] is the filter state vector for the filter update, andΔw[n] is a steepest descent vector for the estimated direction of thesteepest descent, which is related to an error vector (e[n]) for theadaptation error signal by a Jacobian matrix, denoted e=J(Δw).
 3. Thecircuit of claim 2, wherein an update to the filter state vector isw[n+1]=w[n]−μ(J^(H)J)⁻¹(J^(H)e[n]), where [Δw[n]=(J^(H)J)⁻¹(J^(H)e[n])]is based on a least-squares solution for the estimated direction ofsteepest descent.
 4. The circuit of claim 1, wherein an IQ mismatch inthe TX RX unit, which receives as input TX baseband data y(n), ismodeled by a y(n)_direct transfer function g₁(f) receiving y(n) asinput, and a complex conjugate y(n)_image transfer function g₂(f)receiving a complex conjugate of y(n) as input such that direct learningadaption is based on: (a) ∥g₁(f)∥≈1, (b) ∥g₂(f)∥ is in the range of 30dB less than ∥g₁(f)∥, and (c) ∥1−g₁(f)<1.
 5. The circuit of claim 4,wherein the FBRX is based on a direct conversion architecture, includingperforming analog IQ downconversion and demodulation to provide the FBRXbaseband data z(n) without introducing significant IQ mismatchassociated with downconversion and IQ demodulation.
 6. The circuit ofclaim 1, wherein the TX IQ_mismatch pre-compensator is furtherconfigured to estimate a phase error between the TX analog unitupconversion and the FBRX unit downconversion, and compensate for thatphase error according to the phase of a complex number generated in theDL adapter by a cross-correlation between the TX baseband data x(n) andthe FBRX baseband data z(n).
 7. The circuit of claim 1, wherein the TXIQ_mismatch pre-compensator performs direct learning adaptation toupdate the IQ pre-comp filters w1 and w2 based on a linear combinationof direct learning adaptations for successive data blocks, where thestate of the IQ pre-comp filters w1 and w2 is fixed during data blockcapture.
 8. The circuit of claim 1, wherein the TX IQ_mismatchpre-compensator is widely linear, and one of a digital signal processorand a hardware accelerator that perform the following functions: TXdelay estimation, FBRX delay estimation, TX/FBRX error signalgeneration, and DL adaptation.
 9. The circuit of claim 1, the TX RF unithaving an IQ mismatch associated with IQ modulation and upconversion,manifested as an IQ_mismatch image, and the TX IQ_mismatchpre-compensator performing IQ_mismatch pre-compensation filtering suchthat the pre-compensated TX baseband data y(n) manifests a compensationimage to interfere destructively with the IQ_mismatch image associatedwith the TX analog unit.
 10. A wireless transmitter circuit based on adirect conversion architecture, comprising: a TX (transmit) signal chaincoupled to receive digital TX baseband data x(n), and including adigital TX IQ_mismatch pre-compensator to compensate for in-phase (I)and quadrature (Q) mismatch, the TX IQ_mismatch pre-compensator coupledto receive the digital TX baseband data x(n) and provide digitalpre-compensated TX baseband data y(n) by performing IQ pre-compensationfiltering based on IQ pre-comp filters w1 and w2; an analog TX RF (radiofrequency) unit to generate an upconverted TX RF signal based on thepre-compensated TX baseband data y(n); a feedback receiver circuit(FBRX), coupled to receive the TX RF signal, and to generatecorresponding FBRX baseband data z(n) based on downconverting the TX RFsignal; and an adaptation module coupled to the TX IQ_mismatchpre-compensator to perform direct learning adaptation of the IQ pre-compfilters w1 and w2, modeled respectively as an x(n)_direct transferfunction w1 receiving x(n) as input, and a complex conjugate x(n)_imagetransfer function w2 receiving a complex conjugate of x(n) as input, theadaptation module coupled to receive the TX baseband data x(n), and thecorresponding FBRX baseband data z(n); the adaptation module including:a TX delay estimator configured to modify x(n) corresponding to a delaythrough the TX RF unit, producing a delay x(n) signal; a FBRX delayestimator configured to modify z(n) corresponding to a delay through theFBRX unit, producing a delay z(n) signal; a TX/FBRX error signalgenerator configured to generate an adaptation error signalcorresponding to a difference between the delay x(n) and delay z(n)signals; and a DL (direct learning) adapter to perform direct learningadaptation to adjust the IQ pre-comp filters w1 and w2 to minimize theadaptation error signal.
 11. The circuit of claim 10, wherein the DLadapter is configured perform a direct learning adaptation thatconverges in the direction of an estimated steepest descent, accordingto, where w[n] is the filter state vector for the filter update, andΔw[n] is a steepest descent vector for the estimated direction of thesteepest descent, which is related to an error vector (e[n]) for theadaptation error signal by a Jacobian matrix, denoted e=J(Δw).
 12. Thecircuit of claim 11, wherein an update to the filter state vector isw[n+1]=w[n]−μ(J^(H)J)⁻¹(J^(H)e[n]), where [Δw[n]=(J^(H)J)⁻¹(J^(H)e[n])]is based on a least-squares solution for the estimated direction ofsteepest descent.
 13. The circuit of claim 10, wherein an IQ mismatch inthe TX RF unit, which receives as input TX baseband data y(n), ismodeled by a y(n)_direct transfer function g₁(f) receiving y(n) asinput, and a complex conjugate y(n)_image transfer function g₂(f)receiving a complex conjugate of y(n) as input such that direct learningadaption is based on: (a) ∥g₁(f)∥≈1, (b) ∥g₂(f)∥ is in the range of 30dBless than ∥g₁(f)∥, and (c) ∥1−g₁(f)∥<1.
 14. The circuit of claim 13,wherein the FBRX is based on a direct conversion architecture, includingperforming analog IQ downconversion and demodulation to provide the FBRXbaseband data z(n) without introducing significant IQ mismatchassociated with downconversion and IQ demodulation.
 15. The circuit ofclaim 10, wherein the TX IQ_mismatch pre-compensator is furtherconfigured to estimate a phase error between the TX analog unitupconversion and the FBRX unit downconversion, and compensate for thatphase error according to the phase of a complex number generated in theDL adapter by a cross-correlation between the TX baseband data x(n) andthe FBRX baseband data z(n).
 16. The circuit of claim 10, wherein the TXIQ_mismatch pre-compensator performs direct learning adaptation toupdate the IQ pre-comp filters w1 and w2 based on a linear combinationof direct learning adaptations for successive data blocks, where thestate of the IQ pre-comp filters w1 and w2 is fixed during data blockcapture.
 17. The circuit of claim 10, wherein the TX IQ_mismatchpre-compensator is widely linear, and one of a digital signal processorand a hardware accelerator that perform the following functions: TXdelay estimation, FBRX delay estimation, TX/FBRX error signalgeneration, and DL adaptation.
 18. The circuit of claim 10, the TX RFunit having an IQ mismatch associated with IQ modulation andupconversion, manifested as an IQ_mismatch image, and the TX IQ_mismatchpre-compensator performing IQ_mismatch pre-compensation filtering suchthat the pre-compensated TX baseband data y(n) manifests a compensationimage to interfere destructively with the IQ_mismatch image associatedwith the TX analog unit.
 19. A method for use in direct conversion radiofrequency transmission, comprising: receiving in a transmit signal chaindigital TX baseband data x(n), and generating corresponding TX RF (radiofrequency) signals, including IQ (in-phase I and quadrature Q)modulation and upconversion, where IQ modulation and upconversionresults in the generation of IQ mismatch signals; generating, from theTX baseband data x(n), pre-compensated TX baseband data y(n) tocompensate for the IQ mismatch signals by performing IQ pre-compensationfiltering based on IQ pre-comp filters w1 and w2; receiving in afeedback receiver (FBRX) the TX RF signal, and generating correspondingFBRX baseband data z(n) based on downconverting the TX RF signal; andoperating on the TX baseband data x(n), and the corresponding FBRXbaseband data z(n) to perform a direct learning adaptation of the IQpre-comp filters w1 and w2, including modeling the filters w1 and w2respectively as an x(n)_direct transfer function w1 receiving x(n) asinput, and a complex conjugate x(n)_image transfer function w2 receivinga complex conjugate of x(n) as input, including: modifying x(n)corresponding to a delay in generating the TX RF signals, producing adelay x(n) signal; modifying z(n) corresponding to a delay in generatingthe FBRX baseband data z(n), producing a delay z(n) signal; generatingan adaptation error signal corresponding to a difference between thedelay x(n) and delay z(n) signals; and performing direct learningadaptation to adjust the IQ pre-comp filters w1 and w2 to minimize theadaptation error signal.